Research on prediction model of learning behavior of clinical medical postgraduates-comparative analysis of linear regression and machine learning
10.3760/cma.j.cn116021-20200228-00439
- VernacularTitle:临床医学研究生学习行为的预测模型研究--线性回归和机器学习的对比分析
- Author:
Chi ZHANG
;
Yanqing LI
;
Deping LIU
;
Peizhong WANG
;
Tianzhi CHEN
;
Wenzhuo LI
;
Jinzhong JIA
- From:
Chinese Journal of Medical Education Research
2021;20(3):350-355
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To compare the prediction efficiency of traditional linear regression model and four machine learning models on the learning behavior of clinical medical postgraduates, and to explore the pros and cons and applicability of different prediction models.Methods:A total of 6,922 clinical medical postgraduates were surveyed, their comprehensive learning behavior scores were obtained through the learning behavior scale. In the training set, Lasso linear regression and artificial neural network, decision tree, Bootstrap random forest, and lifting tree were used to build prediction models respectively. The above models were used to predict the validation set data and compare the prediction efficiency.Results:The comprehensive learning behavior score of clinical medical postgraduates was (3.31±0.54) points, and the overall compliance rate was 74.02%. In the linear regression model, the influence of age, school level, degree type, learning interest, pressure and satisfaction on learning behavior were statistically significant. In the prediction of validation set, the sensitivity, specificity, and accuracy of the linear regression model were 0.484, 0.914, and 0.801, respectively. The indexes of the four machine learning models were higher than those of the traditional linear regression model, and the Bootstrap random forest had the highest elevation.Conclusion:The linear regression model has a good prediction effect on learning behavior, and machine learning is superior to linear regression model in terms of accuracy of prediction. However, traditional linear regression models are superior to machine learning models in computational efficiency and interpretability.
